Goal-Oriented Status Updating for Real-time Remote Inference over Networks with Two-Way Delay
This work addresses the challenge of efficient data scheduling for remote AI inference in real-time applications, such as IoT or autonomous systems, by extending prior models to handle non-monotone age dependence and Markovian delays, representing an incremental improvement over existing methods.
The paper tackles the problem of scheduling data transmissions for real-time remote inference over networks with two-way delays, aiming to minimize inference error by optimizing packet freshness, length, and transmission timing. The result is a closed-form solution and index-based threshold policies that reduce inference error to one-sixth compared to age-based scheduling of unit-length packets.
We study a setting where an intelligent model (e.g., a pre-trained neural network) infers the real-time value of a target signal using data samples transmitted from a remote source. The transmission scheduler decides (i) the freshness of packets, (ii) their length (i.e., the number of samples they contain), and (iii) when they should be transmitted. The freshness is quantified using the Age of Information (AoI), and the inference quality for a given packet length is a general function of AoI. Previous works assumed i.i.d. transmission delays with immediate feedback or were restricted to the case where inference performance degrades as the input data ages. Our formulation, in addition to capturing non-monotone age dependence, also covers Markovian delay on both forward and feedback links. We model this as an infinite-horizon average-cost Semi-Markov Decision Process. We obtain a closed-form solution that decides on (i) and (iii) for any constant packet length. The solution for when to transmit is an index-based threshold policy, where the index function is expressed in terms of the delay state and AoI at the receiver. In contrast, the freshness of the selected packet is a function of only the delay state. We then separately optimize the value of the constant packet length. Moreover, we also develop an index-based threshold policy for the time-variable packet length case, which allows a complexity reduction. In simulation results, we observe that our goal-oriented scheduler drops inference error down to one-sixth with respect to the age-based scheduling of unit-length packets.